#include "../llama-build-context.h"
#include "../llama-model.h"
#include "../llama-context.h"

ggml_cgraph * llm_build_context::build_phi3() {
    struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(n_tokens), false);

    const int64_t n_embd_head = hparams.n_embd_head_v(0);
    const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));

    struct ggml_tensor * cur;
    struct ggml_tensor * inpL;

    inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

    // inp_pos - contains the positions
    struct ggml_tensor * inp_pos = build_inp_pos();

    // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
    struct ggml_tensor * KQ_mask;
    if (hparams.n_swa == 0) {
        // Phi-4 does not use SWA
        KQ_mask = build_inp_KQ_mask();
    }
    else {
        KQ_mask = build_inp_KQ_mask_swa();
    }

    for (int il = 0; il < n_layer; ++il) {
        auto residual = inpL;

        // self-attention
        {
            // rope freq factors for 128k context
            struct ggml_tensor * rope_factors = build_rope_factors(il);

            struct ggml_tensor * attn_norm_output = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
            cb(attn_norm_output, "attn_norm", il);

            struct ggml_tensor * Qcur = nullptr;
            struct ggml_tensor * Kcur = nullptr;
            struct ggml_tensor * Vcur = nullptr;

            if (model.layers[il].wqkv) {
                cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
                cb(cur, "wqkv", il);

                Qcur = ggml_view_3d(ctx0, cur, n_embd_head,    n_head, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0 * sizeof(float) * (n_embd));
                Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1 * sizeof(float) * (n_embd));
                Vcur = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
            }
            else {
                Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
                Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
                Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
            }

            cb(Qcur, "Qcur", il);
            cb(Kcur, "Kcur", il);
            cb(Vcur, "Vcur", il);

            Qcur = ggml_rope_ext(
                    ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                    );
            cb(Qcur, "Qcur", il);

            Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
            cb(Qcur, "Qcur", il);

            Kcur = ggml_rope_ext(
                    ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                    );
            cb(Kcur, "Kcur", il);

            cur = llm_build_kv(ctx0, lctx, kv_self, gf,
                    model.layers[il].wo, model.layers[il].bo,
                    Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
        }

        if (il == n_layer - 1) {
            // skip computing output for unused tokens
            struct ggml_tensor * inp_out_ids = build_inp_out_ids();
            cur = ggml_get_rows(ctx0, cur, inp_out_ids);
            residual = ggml_get_rows(ctx0, residual, inp_out_ids);
        }

        cur = ggml_add(ctx0, cur, residual);
        residual = cur;

        // FF
        // special-case: the up and gate tensors are merged into a single tensor
        // TOOD: support into llm_build_ffn
        {
            cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
                    model.layers[il].ffn_up,   NULL, NULL,
                    NULL,                      NULL, NULL,
                    model.layers[il].ffn_down, NULL, NULL,
                    NULL,
                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
            cb(cur, "ffn_out", il);
        }

        cur = ggml_add(ctx0, residual, cur);
        cur = lctx.cvec.apply_to(ctx0, cur, il);
        cb(cur, "l_out", il);

        // input for next layer
        inpL = cur;
    }

    cur = llm_build_norm(ctx0, inpL, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
    cb(cur, "result_norm", -1);

    cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
    cb(cur, "result_output", -1);

    ggml_build_forward_expand(gf, cur);

    return gf;
}